There are several advantages that may occur from patient-specific orthopedic implants and surgeries including exact sizing of the implant according to the anatomy, such as reducing operating time, improving performance, etc.
When designing and conceiving patient-specific orthopedic implants and planning surgeries, including alignment guides, all relevant components of an anatomical structure, e.g. an articulation, are to be modeled and segmented with high precision. Precise modelling and segmentation in turn ensures that the resulting prostheses and alignment guides accurately fit the unique shape and size of the anatomical structure. Furthermore, segmentation of bones from 3D images is important to many clinical applications such as visualization, enhancement, disease diagnosis, implant design, cutting guide design, and surgical planning.
In the field of bone imaging, many imaging techniques and methods are known in the art in order to produce a skeletal model, such as a 3D skeletal model, of at least a portion of a body structure of a patient, such as a bone or series of bones on/between which an orthopedic implant is to be implanted.
For example and without being limitative, common imaging techniques, including magnetic resonance imaging (MRI), computed axial tomography (CAT scan), ultrasound, or the like are combined with three-dimensional image reconstruction tools, such as CAD software or the like, for the three-dimensional image reconstruction. In the case of bones with well-defined joints, known imaging techniques and three-dimensional image reconstruction tools are usually able to produce satisfactory models.
However, in the case of small and/or multiple adjacent bones such as, for example, bones of the hands and foot, where the distance between the bones is relatively small, thereby forming closely matching joints therebetween, and larger bones with outer edges closely matching one another, thereby also defining closely matching joints, such as hip bones or the like, known imaging techniques and three-dimensional image reconstruction tools often prove inadequate to perform the required individual multi-bone segmentation, i.e. the partitioning of a digital image into multiple segments in order to provide data that can be used for generating the three-dimensional image which clearly define the shape of each bone of the joints and is therefore more meaningful or easier to analyze. In fact, the failure of those well-known imaging techniques to segment these images is due to the challenging nature of the acquired images. For example, in CT imagery, when the boundaries of two bones are too close to each other, as described earlier, they tend to be diffused, which lower the contrast of the boundaries of the neighboring bones with respect to the background. Moreover, the bone structures have inhomogeneous intensities which involve an overlap between the distributions of the intensities within the regions.
In view of the above, there is a need for an improved method and system for performing bone segmentation which would be able to overcome or at least minimize some of the above-discussed prior art concerns.